Percorrer por autor "Tavares, Inês"
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- Data mining techniques for electricity customer characterizationPublication . Ramos, Sérgio Filipe Carvalho; Soares, João; Cembranel, Samuel S.; Tavares, Inês; Foroozandeh, Z.; Vale, Zita; Fernandes, RubipiaraThe liberalization of electricity markets has been resulted in the emergence of new players, increasing the competitiveness in the markets, standing those can provide better services for better prices. The knowledge of energy consumers’ profile has been an important tool to help players to make decisions in the electrical sectors. In this paper, a characterization model of typical load curves for Low Voltage (LV) customers is proposed and evaluated. The identification of consumption patterns is based on clustering analysis. The clustering methodology is based on seven algorithms, partitional and hierarchical. Also, five clustering validity indices are used to identify the best data partition. With the knowledge obtained in clustering analysis, a classification model is used to classify new customers according to their consumption data. The classification model is used to select the correct class for each customer. To make the model simple, each load curve is represented by three indices which represent load curves shape. The methodology used in this work demonstrates to be an effective tool and can be used in most diverse sectors, highlighting the use of knowledge in the optimization of the energy contracting for low voltage customers. The energy consumption data can be constantly updated to improve the model precision, finding estimates that can better represent consumers and their consumption habits.
- Optimizing Energy Consumption of Household Appliances Using PSO and GWOPublication . Tavares, Inês; Almeida, José; Soares, João; Ramos, Sérgio; Vale, Zita; Foroozandeh, ZahraDue to the increasing electricity consumption in the residential sector, new control systems emerged to control the demand side. Some techniques have been developed, such as shaping the curve’s load peaks by planning and shifting the electricity demand for household appliances. This paper presents a comparative analysis for the energy consumption optimization of two household appliances using two Swarm Intelligence (SI) algorithms: Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). This problem’s main objective is to minimize the energy cost according to both machines’ energy consumption, respecting the restrictions applied. Three scenarios are presented: changing the energy market price during the day according to three types of energy tariffs. The results show that the user in the cheapest periods could switch on both machines because both techniques presented the highest energy consumption values. Regarding the objective function analysis, PSO and GWO obtained the best (more economical) values for the simple tariff due to its lower energy consumption. The GWO technique also presented more diverging values from the average objective function value than the PSO algorithm.